问PyTorch中神经网络决策边界的绘制

EN

class NeuralNetwork(torch.nn.Module):
  def __init__(self):
    super(NeuralNetwork, self).__init__()
    self.fc1 = torch.nn.Linear(23, 16)
    self.fc2 = torch.nn.Linear(16, 14)
    self.fc3 = torch.nn.Linear(14, 10)
    self.fc4 = torch.nn.Linear(10, 5)
    self.fc5 = torch.nn.Linear(5, 1)

  def forward(self, x):
    x = torch.relu(self.fc1(x))
    x = torch.relu(self.fc2(x))
    x = torch.relu(self.fc3(x))
    x = torch.relu(self.fc4(x))
    x = torch.sigmoid(self.fc5(x))
    return x

model = NeuralNetwork().double()

CUDA = torch.cuda.is_available()
if CUDA:
  model.cuda()

criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9)

model_1.train()

Precision = []
Cost = []

for epoch in range(10001):

  if CUDA:
    inputs = X_train.cuda()
    label = Y_train.cuda()
  else:
    inputs = X_train
    label = Y_train

  prediction = model_1(inputs)
  loss = criterion(prediction, label)
  accuracy = ((prediction > 0.5) == label).float().mean().item()

  Cost.append(loss.item())
  Precision.append(accuracy)

  if epoch % 1000 == 0 or epoch == 30000:
    print("Epoch:", epoch, ",", "Loss:", loss.item(), ",", "Accuracy:", accuracy)

  # Backpropagation process
  optimizer.zero_grad()
  loss.backward()
  optimizer.step()

model_1.eval()

X_test = torch.from_numpy(X[27000:,:])
Y_test = torch.from_numpy(y[27000:,:]).double()

with torch.no_grad():

  y_pred = model_1(X_test)
  print("Accuracy: ", ((y_pred > 0.5) == Y_test).float().mean().item())

# I've chosen two features because the data is multi-dimensional
X0 = X_test[:,[0,]].reshape(3000)
X5 = X_test[:,[5,]].reshape(3000)
X0, X5 = np.meshgrid(X0, X5)

xx, yy = np.meshgrid(X0, X5)
grid = np.c_[xx.ravel(), yy.ravel()]
probs = y_pred

f, ax = plt.subplots(figsize=(8, 6))
contour = ax.contourf(xx, yy, probs, 25, cmap="RdBu",
                  vmin=0, vmax=1)
ax_c = f.colorbar(contour)
ax_c.set_label("$P(y = 1)$")
ax_c.set_ticks([0, .25, .5, .75, 1])

ax.scatter(X0, X5, c=Y_test, s=50,cmap="RdBu", vmin=-.2, vmax=1.2,edgecolor="white", linewidth=1)

ax.set(aspect="equal",
   xlim=(-5, 5), ylim=(-5, 5),
   xlabel="$X0$", ylabel="$X5$")

TypeError                                 Traceback (most recent call last)

<ipython-input-52-fb941749621a> in <module>()
  1 f, ax = plt.subplots(figsize=(8, 6))
  2 contour = ax.contourf(xx, yy, probs, 25, cmap="RdBu",
----> 3                       vmin=0, vmax=1)
  4 ax_c = f.colorbar(contour)
  5 ax_c.set_label("$P(y = 1)$")

5 frames

/usr/local/lib/python3.6/dist-packages/matplotlib/contour.py in _check_xyz(self, args, kwargs)
1549             raise TypeError("Input z must be a 2D array.")
1550         elif z.shape[0] < 2 or z.shape[1] < 2:
-> 1551             raise TypeError("Input z must be at least a 2x2 array.")
1552         else:
1553             Ny, Nx = z.shape

TypeError: Input z must be at least a 2x2 array.